Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

PURPOSE: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. METHODS: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. RESULTS: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. CONCLUSION: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

Towards assessing and improving the reliability of ultrashort echo time quantitative magnetization transfer (UTE-qMT) MRI of cortical bone: In silico and ex vivo study.

OBJECTIVE: To assess and improve the reliability of the ultrashort echo time quantitative magnetization transfer (UTE-qMT) modeling of the cortical bone. MATERIALS AND METHODS: Simulation-based digital phantoms were created that mimic the UTE-qMT properties of cortical bones. A wide range of SNR from 25 to 200 was simulated by adding different levels of noise to the synthesized MT-weighted images to assess the effect of SNR on UTE-qMT fitting results. Tensor-based denoising algorithm was applied to improve the fitting results. These results from digital phantom studies were validated via ex vivo rat leg bone scans. RESULTS: The selection of initial points for nonlinear fitting and the number of data points tested for qMT analysis have minimal effect on the fitting result. Magnetization exchange rate measurements are highly dependent on the SNR of raw images, which can be substantially improved with an appropriate denoising algorithm that gives similar fitting results from the raw images with an 8-fold higher SNR. DISCUSSION: The digital phantom approach enables the assessment of the reliability of bone UTE-qMT fitting by providing the known ground truth. These findings can be utilized for optimizing the data acquisition and analysis pipeline for UTE-qMT imaging of cortical bones.

Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.

We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and simulated data generation. (1) DL-based attenuation correction (DLAC) remains an area of limited exploration for pediatric whole-body PET/MR and lung-specific DLAC due to data shortages and technical limitations. (2) DL-based image enhancement approximating MR-guided regularized reconstruction with a high-resolution MR prior has shown promise in enhancing PET image quality. However, its clinical value has not been thoroughly evaluated across various radiotracers, and applications outside the head may pose challenges due to motion artifacts. (3) Robust training for DL-based motion correction requires pairs of motion-corrupted and motion-corrected PET/MR data. However, these pairs are rare. (4) DL-based approaches can address the limitations of dynamic PET, such as long scan durations that may cause patient discomfort and motion, providing new research opportunities. (5) Monte-Carlo simulations using anthropomorphic digital phantoms can provide extensive datasets to address the shortage of clinical data. This summary of technical/clinical challenges and potential solutions may provide research opportunities for the research community towards the clinical translation of DL solutions.

Toward a realistic in silico abdominal phantom for QSM.

PURPOSE: QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms. METHODS: Synthetic susceptibility and R 2 * $$ {R}_2^{\ast } $$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility and R 2 * $$ {R}_2^{\ast } $$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM and R 2 * $$ {R}_2^{\ast } $$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms. RESULTS: QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms. CONCLUSION: The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility, R 2 * $$ {R}_2^{\ast } $$ and TEs, and thus creates different abdominal scenarios.

A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study.

BACKGROUND: Mammographic digital imaging is based on X-ray sensors with solid image quality characteristics. These primarily include (a) a response curve that yields high contrast and image latitude, (b) a frequency response given by the Modulation Transfer Function (MTF), which enables small detail imaging and (c) the Normalize Noise Power Spectrum (NNPS) that shows the extent of the noise effect on image clarity. METHODS: In this work, a methodological approach is introduced and described for creating digital phantom images based on the measured image quality properties of the sensor. For this purpose, a mathematical phantom, simulating breast tissue and lesions of blood, adipose, muscle, Ca and Ca(50%)-P(50%) was created by considering the corresponding X-ray attenuation coefficients. The simulated irradiation conditions of the phantom used four mammographic spectra assuming exponential attenuation. Published data regarding noise and blur of a commercial RadEye HR CMOS imaging sensor were used as input data for the resulting images. RESULTS: It was found that the Ca and Ca(50%)-P(50%) lesions were visible in all exposure conditions. In addition, the W/Rh spectrum at 28 kVp provided more detailed images than the corresponding Mo/Mo spectrum. CONCLUSIONS: The presented methodology can act complementarily to image quality measurements, leading to initial optimization of the X-ray exposure parameters per clinical condition.

Normal lung tissue complication probability in MR-Linac and conventional radiotherapy.

PURPOSE: To study normal lung tissue (NLT) complications in magnetic resonance (MR) image based linac and conventional radiotherapy (RT) techniques. MATERIALS AND METHODS: The Geant4 toolkit was used to simulate a 6 MV photon beam. A homogenous magnetic field of 1.5 Tesla (T) was applied in both perpendicular and parallel directions relative to the radiation beam.Analysis of the NLT complications was assessed according to the normal lung tissue complication probability (NTCP), the mean lung dose (MLD), and percentage of the lung volume receiving doses greater than 20 Gy (V20), using a sample set of CT images generated from a commercially available 4D-XCAT digital phantom. RESULTS: The results show that the MLD and V20 were lower for MR-linac RT. The largest reduction of MLD and V20 for MR-linac RT configurations were 5 Gy and 29.3%, respectively. CONCLUSION: MR-linac RT may result in lower NLT complications when compared to conventional RT.

Realistic 4D MRI abdominal phantom for the evaluation and comparison of acquisition and reconstruction techniques.

PURPOSE: This work presents a 4D numerical abdominal phantom, which includes T1 and T2 relaxation times, proton density fat fraction, perfusion, and diffusion, as well as respiratory motion for the evaluation and comparison of acquisition and reconstruction techniques. METHODS: The 3D anatomical mesh models were non-rigidly scaled and shifted by respiratory motion derived from an in vivo scan. A time series of voxelized 3D abdominal phantom images were obtained with contrast determined by the tissue properties and pulse sequence parameters. Two example simulations: (1) 3D T1 mapping under breath-hold and free-breathing acquisition conditions and (2) two different reconstruction techniques for accelerated 3D dynamic contrast-enhanced MRI, are presented. The source codes can be found at https://github.com/SeiberlichLab/Abdominal_MR_Phantom. RESULTS: The proposed 4D abdominal phantom can successfully simulate images and MRI data with nonrigid respiratory motion and specific contrast settings and data sampling schemes. In example 1, the use of a numerical 4D abdominal phantom was demonstrated to aid in the comparison between different approaches for volumetric T1 mapping. In example 2, the average arterial fraction over the healthy hepatic parenchyma as calculated with spiral generalized autocalibrating partial parallel acquisition was closer to that from the fully sampled data than the arterial fraction from conjugate gradient sensitivity encoding, although both are elevated compared to the gold-standard reference. CONCLUSION: This realistic abdominal MR phantom can be used to simulate different pulse sequences and data sampling schemes for the comparison of acquisition and reconstruction methods under controlled conditions that are impossible or prohibitively difficult to perform in vivo.

[Development of New Digital Phantom Creation Tool for Evaluation of Low-contrast Detectability Using Iterative Reconstruction].

PURPOSE: We developed a novel digital phantom-creation tool that will help formulate the standard shooting method for a three-phase dynamic liver study. Here, we present data demonstrating the usefulness of this tool in the assessment of low-contrast detectability and visibility. METHODS: We performed a visual evaluation by adding a spherical digital phantom with a diameter of 8 mm and a computed tomography (CT) value difference of 10 Hounsfield unit (HU) to images taken using filtered back projection and seven types of adaptive iterative dose reduction 3D (Weak, Mild, eMild, Standard, eStandard, Strong, and eStrong). We also examined the partial-volume effect by drawing a profile curve using a digital phantom with a CT value difference of 30 HU and a diameter of 5 mm. Furthermore, a digital phantom with two kinds of filters (smoothing and Gaussian) was added to the image of the home-made simulated tumor phantom to visual valuate its visibility in the phantom's low-contrast module and the digital phantom. RESULTS: Detection sensitivity was significantly decreased in Standard, eStandard, Strong, and eStrong, and the area under the curve also decreased in a similar fashion. We confirmed that the partial-volume effect was due to the different maximum CT values in the profile curve at 4 and 5 mm thickness. The visibility of the low-contrast module and digital phantom was most consistent when using the Gaussian filter. CONCLUSION: This tool can be used for low-contrast detection ability evaluation.

Development of Realistic Striatal Digital Brain (SDB) Phantom for 123I-FP-CIT SPECT and Effect on Ventricle in the Brain for Semi-quantitative Index of Specific Binding Ratio.

PURPOSE: This study aimed at developing the realistic striatal digital brain (SDB) phantom and to assess specific binding ratio (SBR) for ventricular effect in the 123I-FP-CIT SPECT imaging. METHODS: SDB phantom was constructed in to four segments (striatum, ventricle, brain parenchyma, and skull bone) using Percentile method and other image processing in the T2-weighted MR images. The reference image was converted into 128×128 matrixes to align MR images with SPECT images. The process image was reconstructed with projection data sets generated from reference images additive blurring, attenuation, scatter, and statically noise. The SDB phantom was evaluated to find the accuracy of calculated SBR and to find the effect of SBR with/without ventricular counts with the reference and process images. RESULTS: We developed and investigated the utility of the SDB phantom in the 123I-FP-CIT SPECT clinical study. The true value of SBR was just marched to calculate SBR from reference and process images. The SBR was underestimated 58.0% with ventricular counts in reference image, however, was underestimated 162% with ventricular counts in process images. CONCLUSION: The SDB phantom provides an extremely convenient tool for discovering basic properties of 123I-FP-CIT SPECT clinical study image. It was suggested that the SBR was susceptible to ventricle.

Virtual cell imaging: A review on simulation methods employed in image cytometry.

The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

Modeling the residue function in DSC-MRI simulations: analytical approximation to in vivo data.

PURPOSE: An exponential residue function is commonly used in numerical simulations to assess the accuracy of perfusion quantification using dynamic susceptibility contrast (DSC) MRI. Although this might be a reasonable assumption for normal tissue, microvascular hemodynamics are likely to be significantly altered in pathology. Thus the exponential function may no longer be appropriate and the estimated accuracy of DSC-MRI quantification might be inappropriate. The purpose of this study was to characterize in vivo residue function variations in normal and infarcted tissue in a chronic atherosclerotic disease cohort, and to find the most appropriate model for use in DSC simulations. METHODS: Residue functions were measured in vivo in patients with atherosclerotic disease using a nonparametric Control Point Interpolation method, which has been shown to provide a robust characterization of the shape of the residue function. The observed residue functions were approximated with five commonly used analytical expressions: exponential, bi-exponential, Lorentzian, and Fermi functions, and a previously proposed Vascular Model. RESULTS: The lowest error was found with the bi-exponential function approximations to the in vivo residue functions from both normal and infarcted tissue. CONCLUSION: A bi-exponential model should therefore be used in future numerical simulations of DSC-MRI instead of the exponential function.

Performance Evaluation of Deformable Image Registration Systems - SmartAdapt® and Velocity™.

Aim: To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems. Materials and Methods: TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results. Results: The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt® and Velocity™. Conclusion: The DIR algorithms of SmartAdapt® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.

[Evaluation of Low-contrast Detectability Using the Digital Phantom Creation Tool in the Late Arterial Phase to Detect Liver Mass Lesions].

PURPOSE: Late arterial phase images of SD 8, SD 10, and SD 12 were acquired in the 3-phase dynamic study of the liver in combination with hybrid iterative reconstruction. We evaluated the low-contrast detectability by adding a simulated tumor to these images and aimed to formulate a standard image quality. METHODS: We prepared images with and without signal for 60 series of 20 samples, each with 3 image quality types (total: 120 series). The continuous confidence method by 10 observers detected 60 simulated tumors. RESULTS: The detection sensitivities were 0.765, 0.785, and 0.260 for SD 8, SD 10, and SD 12, respectively (p<0.001) with no significantly different specificities, and the areas under the curve were 0.901, 0.892, and 0.616 (p<0.001), respectively. The simulated mass detection rates were 74.5%, 75.0%, and 21.5% for SD 8, SD 10, and SD 12, respectively (p<0.001), and the intraclass correlation coefficients, which indicate interobserver reliability, were 0.697 at SD 10 without signal, and SD 12 without a signal significantly dropped to 0.185. CONCLUSION: Therefore, SD 12 images increase the possibility of overlooking lesions. Hence, image quality in the late arterial phase should be SD 10 or less.

A model for gastrointestinal tract motility in a 4D imaging phantom of human anatomy.

BACKGROUND: Gastrointestinal (GI) tract motility is one of the main sources for intra/inter-fraction variability and uncertainty in radiation therapy for abdominal targets. Models for GI motility can improve the assessment of delivered dose and contribute to the development, testing, and validation of deformable image registration (DIR) and dose-accumulation algorithms. PURPOSE: To implement GI tract motion in the 4D extended cardiac-torso (XCAT) digital phantom of human anatomy. MATERIALS AND METHODS: Motility modes that exhibit large amplitude changes in the diameter of the GI tract and may persist over timescales comparable to online adaptive planning and radiotherapy delivery were identified based on literature research. Search criteria included amplitude changes larger than planning risk volume expansions and durations of the order of tens of minutes. The following modes were identified: peristalsis, rhythmic segmentation, high amplitude propagating contractions (HAPCs), and tonic contractions. Peristalsis and rhythmic segmentations were modeled by traveling and standing sinusoidal waves. HAPCs and tonic contractions were modeled by traveling and stationary Gaussian waves. Wave dispersion in the temporal and spatial domain was implemented by linear, exponential, and inverse power law functions. Modeling functions were applied to the control points of the nonuniform rational B-spline surfaces defined in the reference XCAT library. GI motility was combined with the cardiac and respiratory motions available in the standard 4D-XCAT phantom. Default model parameters were estimated based on the analysis of cine MRI acquisitions in 10 patients treated in a 1.5T MR-linac. RESULTS: We demonstrate the ability to generate realistic 4D multimodal images that simulate GI motility combined with respiratory and cardiac motion. All modes of motility, except tonic contractions, were observed in the analysis of our cine MRI acquisitions. Peristalsis was the most common. Default parameters estimated from cine MRI were used as initial values for simulation experiments. It is shown that in patients undergoing stereotactic body radiotherapy for abdominal targets, the effects of GI motility can be comparable or larger than the effects of respiratory motion. CONCLUSION: The digital phantom provides realistic models to aid in medical imaging and radiation therapy research. The addition of GI motility will further contribute to the development, testing, and validation of DIR and dose accumulation algorithms for MR-guided radiotherapy.

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.

SIGNIFICANCE: Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions. AIM: To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information. APPROACH: A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction. RESULTS: The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%. CONCLUSIONS: A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.

Comparison of normal tissue doses in deep inspiration breath-hold and free breathing methods for radiotherapy of left-sided breast cancer using 4D-XCAT digital phantom.

Purpose: To evaluate normal lung and heart tissue doses for treatment of left-sided breast cancer in deep inspiration breath-hold (DIBH) and free breathing (FB) as a function of breast size and diaphragm displacement using 4D-XCAT digital phantom in a simulation study. Materials and Methods: 4D-XCAT digital phantom was used to create 36 left-sided breast cancer digital phantom datasets with different breast height (BH) of 40, 50, and 60 mm, breast length (BL) of 16, 17, and 18 mm, and diaphragm excursion of 20, 25, 30, and 35 mm. For each dataset, DIBH and FB treatment plans were prepared using planning computerized radiotherapy-three dimensional (PCRT-3D) treatment planning system (TPS) with superposition computational algorithm. Dose differences in DIBH and FB plans were assessed in terms of mean lung dose (MLD), the lung volume receiving ≤20 Gy (V20), normal tissue complication probability (NTCP) of the lung, mean heart dose (MHD), and the heart volume receiving ≤30 (V30). Results: DIBH reduced mean dose and V20 and NTCP of the lung in all cases, by up to 4.37 Gy, 7.62%, and 18.95%. Mean dose and V30 of the heart were also significantly reduced by 5.02 Gy and 8.23%. Conclusions: The use of DIBH for left-sided breast cancer radiotherapy offers excellent possibilities for sparing critical normal tissue without compromising radiation dose to the target.

Technical Note: Four-dimensional deformable digital phantom for MRI sequence development.

PURPOSE: MR-guided radiotherapy has different requirements for the images than diagnostic radiology, thus requiring development of novel imaging sequences. MRI simulation is an excellent tool for optimizing these new sequences; however, currently available software does not provide all the necessary features. In this paper, we present a digital framework for testing MRI sequences that incorporates anatomical structure, respiratory motion, and realistic presentation of MR physics. METHODS: The extended Cardiac-Torso (XCAT) software was used to create T1 , T2 , and proton density maps that formed the anatomical structure of the phantom. Respiratory motion model was based on the XCAT deformation vector fields, modified to create a motion model driven by a respiration signal. MRI simulation was carried out with JEMRIS, an open source Bloch simulator. We developed an extension for JEMRIS, which calculates the motion of each spin independently, allowing for deformable motion. RESULTS: The performance of the framework was demonstrated through simulating the acquisition of a two-dimensional (2D) cine and demonstrating expected motion ghosts from T2 weighted spin echo acquisitions with different respiratory patterns. All simulations were consistent with behavior previously described in literature. Simulations with deformable motion were not more time consuming than with rigid motion. CONCLUSIONS: We present a deformable four-dimensional (4D) digital phantom framework for MR sequence development. The framework incorporates anatomical structure, realistic breathing patterns, deformable motion, and Bloch simulation to achieve accurate simulation of MRI. This method is particularly relevant for testing novel imaging sequences for the purpose of MR-guided radiotherapy in lungs and abdomen.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation.

PURPOSE: Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. METHODS: Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. RESULTS: Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. CONCLUSIONS: DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Monte Carlo simulation and performance assessment of GE Discovery 690 VCT positron emission tomography/computed tomography scanner.

The aim of this study is to simulate GE Discovery 690 VCT positron emission tomography/computed tomography (PET/CT) scanner using Geant4 Application for Tomographic Emission (GATE) simulation package (version 8). Then, we assess the performance of scanner by comparing measured and simulated parameter results. Detection system and geometry of PET scanner that consists of 13,824 LYSO crystals designed in 256 blocks and 24 ring detectors were modeled. In order to achieve a precise model, we verified scanner model. Validation was based on a comparison between simulation data and experimental results obtained with this scanner in the same situation. Parameters used for validation were sensitivity, spatial resolution, and contrast. Image quality assessment was done based on comparing the contrast recovery coefficient (CRC) of simulated and measured images. The findings demonstrate that the mean difference between simulated and measured sensitivity is <7%. The simulated spatial resolution agreed to within <5.5% of the measured values. Contrast results had a slight divergence within the range below 4%. The image quality validation study demonstrated an acceptable agreement in CRC for 8:1 and 2:1 source-to-background activity ratio. Validated performance parameters showed good agreement between experimental data and simulated results and demonstrated that GATE is a valid simulation tool for simulating this scanner model. The simulated model of this scanner can be used for future studies regarding optimization of image reconstruction algorithms and emission acquisition protocols.

A robust deformable image registration enhancement method based on radial basis function.

BACKGROUND: To develop and evaluate a robust deformable image registration (DIR) enhancement method based on radial basis function (RBF) expansion. METHODS: To improve DIR accuracy using sparsely available measured displacements, it is crucial to estimate the motion correlation between the voxels. In the proposed method, we chose to derive this correlation from the initial displacement vector fields (DVFs), and represent it in the form of RBF expansion coefficients of the voxels. The method consists of three steps: (I) convert an initial DVF to a coefficient matrix comprising expansion coefficients of the Wendland's RBF; (II) modify the coefficient matrix under the guidance of sparely distributed landmarks to generate the post-enhancement coefficient matrix; and (III) convert the post-enhancement coefficient matrix to the post-enhancement DVF. The method was tested on five DIR algorithms using a digital phantom. 3D registration errors were calculated for comparisons between the pre-/post-enhancement DVFs and the ground-truth DVFs. Effects of the number and locations of landmarks on DIR enhancement were evaluated. RESULTS: After applying the DIR enhancement method, the 3D registration errors per voxel (unit: mm) were reduced from pre-enhancement to post-enhancement by 1.3 (2.4 to 1.1, 54.2%), 0.0 (0.9 to 0.9, 0.0%), 6.1 (8.2 to 2.1, 74.4%), 3.2 (4.7 to 1.5, 68.1%), and 1.7 (2.9 to 1.2, 58.6%) for the five tested DIR algorithms respectively. The average DIR error reduction was 2.5±2.3 mm (percentage error reduction: 51.1%±29.1%). 3D registration errors decreased inverse-exponentially as the number of landmarks increased, and were insensitive to the landmarks' locations in relation to the down-sampling DVF grids. CONCLUSIONS: We demonstrated the feasibility of a robust RBF-based method for enhancing DIR accuracy using sparsely distributed landmarks. This method has been shown robust and effective in reducing DVF errors using different numbers and distributions of landmarks for various DIR algorithms.